{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4be335df-0523-41b0-9815-c9d7b2290376",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "import train_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "36f7c015-795a-46ec-b421-d454c7afe014",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "n_output = 4\n",
    "n_epochs = 256\n",
    "batch_size = 64\n",
    "lr_init = 0.01\n",
    "n_input = 7\n",
    "n_hidden = 4\n",
    "seed = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "057211b0-37b6-4b8f-8621-b54fc3f5761b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练数据归一化\n",
    "train_utils.scale_train_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "33d20eff-5463-4a3b-bd83-b4801b52e871",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_input=7, n_output=4, n_hidden=4\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1d440768099448dd935561a2371222c2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/256 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 0\tmean loss: 0.10305120320874955\tcurrent learning rate: 0.01\n",
      "epoch 7\tmean loss: 0.01227051375335822\tcurrent learning rate: 0.009726562500000004\n",
      "epoch 15\tmean loss: 0.01136698016939253\tcurrent learning rate: 0.009414062500000006\n",
      "epoch 23\tmean loss: 0.01061882514877886\tcurrent learning rate: 0.00910156250000001\n",
      "epoch 31\tmean loss: 0.009682055348181336\tcurrent learning rate: 0.00878906250000001\n",
      "epoch 39\tmean loss: 0.009073704145832435\tcurrent learning rate: 0.008476562500000014\n",
      "epoch 47\tmean loss: 0.008616382469942882\tcurrent learning rate: 0.008164062500000015\n",
      "epoch 55\tmean loss: 0.008753859641713702\tcurrent learning rate: 0.007851562500000013\n",
      "epoch 63\tmean loss: 0.008143635316952346\tcurrent learning rate: 0.007539062500000014\n",
      "epoch 71\tmean loss: 0.007837698425964237\tcurrent learning rate: 0.0072265625000000125\n",
      "epoch 79\tmean loss: 0.008119476986882163\tcurrent learning rate: 0.006914062500000012\n",
      "epoch 87\tmean loss: 0.007914543320107277\tcurrent learning rate: 0.006601562500000012\n",
      "epoch 95\tmean loss: 0.00745299555671345\tcurrent learning rate: 0.006289062500000012\n",
      "epoch 103\tmean loss: 0.007272155621571755\tcurrent learning rate: 0.005976562500000011\n",
      "epoch 111\tmean loss: 0.0072573124078369414\tcurrent learning rate: 0.005664062500000011\n",
      "epoch 119\tmean loss: 0.007125028451519284\tcurrent learning rate: 0.005351562500000011\n",
      "epoch 127\tmean loss: 0.007075114426903109\tcurrent learning rate: 0.0050390625000000105\n",
      "epoch 135\tmean loss: 0.007007564219333784\tcurrent learning rate: 0.00472656250000001\n",
      "epoch 143\tmean loss: 0.006655010413249286\tcurrent learning rate: 0.00441406250000001\n",
      "epoch 151\tmean loss: 0.006838847253084481\tcurrent learning rate: 0.00410156250000001\n",
      "epoch 159\tmean loss: 0.0067604783119243565\tcurrent learning rate: 0.0037890625000000094\n",
      "epoch 167\tmean loss: 0.006533273862526855\tcurrent learning rate: 0.0034765625000000087\n",
      "epoch 175\tmean loss: 0.0067516414635360915\tcurrent learning rate: 0.0031640625000000084\n",
      "epoch 183\tmean loss: 0.006453124141096444\tcurrent learning rate: 0.0028515625000000077\n",
      "epoch 191\tmean loss: 0.006372736307403007\tcurrent learning rate: 0.002539062500000007\n",
      "epoch 199\tmean loss: 0.006234050230429421\tcurrent learning rate: 0.0022265625000000067\n",
      "epoch 207\tmean loss: 0.006242242414653253\tcurrent learning rate: 0.0019140625000000063\n",
      "epoch 215\tmean loss: 0.0062022725361152\tcurrent learning rate: 0.0016015625000000047\n",
      "epoch 223\tmean loss: 0.006051037894606271\tcurrent learning rate: 0.001289062500000004\n",
      "epoch 231\tmean loss: 0.006013722714824491\tcurrent learning rate: 0.0009765625000000037\n",
      "epoch 239\tmean loss: 0.00595665807286508\tcurrent learning rate: 0.0006640625000000025\n",
      "epoch 247\tmean loss: 0.00589943265640399\tcurrent learning rate: 0.0003515625000000014\n",
      "epoch 255\tmean loss: 0.005824744809745359\tcurrent learning rate: 3.9062500000000163e-05\n",
      "time used: 342.5661873817444s\n"
     ]
    }
   ],
   "source": [
    "# 训练模型\n",
    "model = train_utils.train_a_model(n_input, n_output, n_hidden, n_epochs, batch_size, lr_init, seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9c5a8a5e-b245-477a-849c-ba5e236ffa09",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 保存模型，可跳过\n",
    "train_utils.save_model(model, n_input, n_hidden, seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "454d110d-482b-410d-b37f-bb6ed22f8e50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.5, 1. ])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.linspace(0, 1, num=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce362f91-fef3-4aa8-b3c1-e1c4e74952d9",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3b16683c-beaf-4501-9f42-b4bf20eb2c8a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import predict_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4ba55194-1418-4cbe-84a7-0ff6f1c78e25",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 载入模型，可跳过\n",
    "model = predict_utils.load_model(n_input, n_output, n_hidden, seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "da68cd42-e4ff-46e1-8225-f219e4731ab7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型参数总数：274\n"
     ]
    }
   ],
   "source": [
    "print(f'模型参数总数：{sum(p.numel() for p in model.parameters() if p.requires_grad)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cf6acf52-1767-4fe0-a71a-ccdf350dd575",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 预测数据预处理\n",
    "predict_utils.scale_predict_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d9d160d1-1829-4a6d-9cd6-a7b7f56ae8c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e36f901216c84f5d93d1cb81502240bb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "29d46c554d6e4f71bd07851ec8696f67",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "be12b898f1ed409fb576d205b8b0e9bd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 对三组数据分别预测\n",
    "submit = []\n",
    "for i in range(3):\n",
    "    submit.append(predict_utils.predict(pd.read_pickle(f'tmp/predict_B_{i}.pkl'), model, n_input, filename=f'{i}'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6ec395e9-fb75-454d-95bb-a925290372b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>dv1</th>\n",
       "      <th>dv2</th>\n",
       "      <th>mv1</th>\n",
       "      <th>cv1</th>\n",
       "      <th>cv2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2023-01-01 23:59:30</td>\n",
       "      <td>0.90</td>\n",
       "      <td>352.00</td>\n",
       "      <td>305.00</td>\n",
       "      <td>333.990000</td>\n",
       "      <td>0.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2023-01-02 00:00:00</td>\n",
       "      <td>0.93</td>\n",
       "      <td>351.62</td>\n",
       "      <td>305.36</td>\n",
       "      <td>333.303215</td>\n",
       "      <td>0.675813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2023-01-02 00:00:30</td>\n",
       "      <td>0.93</td>\n",
       "      <td>352.09</td>\n",
       "      <td>305.33</td>\n",
       "      <td>333.596645</td>\n",
       "      <td>0.676121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2023-01-02 00:01:00</td>\n",
       "      <td>0.92</td>\n",
       "      <td>351.15</td>\n",
       "      <td>304.26</td>\n",
       "      <td>333.633654</td>\n",
       "      <td>0.677721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2023-01-02 00:01:30</td>\n",
       "      <td>0.89</td>\n",
       "      <td>352.26</td>\n",
       "      <td>304.79</td>\n",
       "      <td>333.369695</td>\n",
       "      <td>0.678512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>2023-01-02 16:38:00</td>\n",
       "      <td>1.01</td>\n",
       "      <td>344.87</td>\n",
       "      <td>293.46</td>\n",
       "      <td>317.241419</td>\n",
       "      <td>1.026974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>2023-01-02 16:38:30</td>\n",
       "      <td>1.02</td>\n",
       "      <td>345.07</td>\n",
       "      <td>292.85</td>\n",
       "      <td>317.086552</td>\n",
       "      <td>1.023936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>2023-01-02 16:39:00</td>\n",
       "      <td>1.04</td>\n",
       "      <td>345.31</td>\n",
       "      <td>293.79</td>\n",
       "      <td>317.071697</td>\n",
       "      <td>1.015182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>2023-01-02 16:39:30</td>\n",
       "      <td>1.04</td>\n",
       "      <td>344.47</td>\n",
       "      <td>293.49</td>\n",
       "      <td>317.202411</td>\n",
       "      <td>1.009549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>2023-01-02 16:40:00</td>\n",
       "      <td>1.06</td>\n",
       "      <td>344.78</td>\n",
       "      <td>299.05</td>\n",
       "      <td>316.952327</td>\n",
       "      <td>1.006798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6006 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    time   dv1     dv2     mv1         cv1       cv2\n",
       "6    2023-01-01 23:59:30  0.90  352.00  305.00  333.990000  0.670000\n",
       "7    2023-01-02 00:00:00  0.93  351.62  305.36  333.303215  0.675813\n",
       "8    2023-01-02 00:00:30  0.93  352.09  305.33  333.596645  0.676121\n",
       "9    2023-01-02 00:01:00  0.92  351.15  304.26  333.633654  0.677721\n",
       "10   2023-01-02 00:01:30  0.89  352.26  304.79  333.369695  0.678512\n",
       "...                  ...   ...     ...     ...         ...       ...\n",
       "2003 2023-01-02 16:38:00  1.01  344.87  293.46  317.241419  1.026974\n",
       "2004 2023-01-02 16:38:30  1.02  345.07  292.85  317.086552  1.023936\n",
       "2005 2023-01-02 16:39:00  1.04  345.31  293.79  317.071697  1.015182\n",
       "2006 2023-01-02 16:39:30  1.04  344.47  293.49  317.202411  1.009549\n",
       "2007 2023-01-02 16:40:00  1.06  344.78  299.05  316.952327  1.006798\n",
       "\n",
       "[6006 rows x 6 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将三个预测结果拼起来\n",
    "submit = pd.concat([df.tail(2002) for df in submit])\n",
    "submit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1d080feb-58c7-43d4-9177-d495b26f30af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存结果\n",
    "submit[['cv1', 'cv2']].to_csv(f'submit/predict_{n_input}_{n_hidden}_{seed}.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e77a9dc6-9aec-4cd4-8adc-3b00a7c8ea23",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
